In a variety of fields, limited access to high-quality information about a subject or proposition under analysis may be an obstacle to good decision-making. In some situations, little information is known about the subject or proposition to begin with. In other situations, a great deal of information may be available, but the relative value of each piece of information may be undetermined. A limited amount of high-quality information may be hidden among a large amount of low-quality information. Obtaining information, or determining the value of already-obtained information, may be a long, difficult, and expensive process. However, it is often necessary to undergo such a process in order to make well-informed decisions.
For example, a number of businesses follow a “hits” model, wherein a small percentage of successful investments generate returns substantial enough to compensate for a high percentage of relative (or absolute) failures. Venture capital (“VC”) is an exemplary field that is traditionally viewed as a “hits” business. Timely access to high-quality information may allow businesses such as venture capital to increase their chances of selecting successful investments, thus increasing their success rates and return-on-investment.
One factor contributing to poor decision making, and thus a negative rate of return in fields such as venture capitalism, is a lack of a systematic method for rigorously evaluating a given subject, proposition, result, or option. One possible solution to improve the evaluation process is for decision-makers to acquire more information. However, experience has shown that access to more data may make a decision-maker more informed as to the realities of a business, but not necessarily smarter in interpreting those realities. With the precipitous growth of available information it becomes simply intractable to parse, analyze and make sense of all relevant data.
Typically, when more information is available than are experts to analyze it, organizations add more experts while continuing to mine available data as thoroughly as possible. But there are two problems with this tactic, especially for groups such as venture capitalists, who may participate at an early stage of development and may not have ready access to data about a market that doesn't yet exist.
The first problem with this tactic is the bias of experts. In a typical venture capital group, these experts may come from the ranks of successful investors and entrepreneurs. The downside to this expertise is that experience produces bias, and bias can be a hindrance to objective analysis of new markets and business scenarios. Methodologies or perspectives that have proven successful in the past may not work again.
The second problem with this tactic is the fact that expert predictions rely on subjective factors. For example, in a typical venture capital firm these factors may include prior performance of investments, interpretation of market conditions, ‘au courant’ investment fads, group politics, cultures of contrarian success and other skewing factors. These realities are often directive in decision-making. Consequently, the predictive utility of resultant decisions may be weakened.
FIG. 1 depicts a conventional VC decision-making process 100. The terminus for this process 100 is the prediction 130. Experts are given a number of initial inputs 110, which are combined and analyzed in a forecasting process 120. Exemplary considerations 200 that may be used as inputs in a VC decision-making model are shown in FIG. 2. Before investing in a firm, a venture capitalist may consider factors related to the market that the firm is involved with 210. Exemplary factors include the size of the market 212 and the degree of competitiveness of the market 218, whether the VC expects the market to grow or contract 214, and the rate of market adoption 216. A venture capitalist will likely also consider the factors related to the firm being considered for investment 220. Such factors may include the novelty or uniqueness of the firm's proposition 222, the protected IP that the firm has acquired 224, the skill and experience of the firm's management 226, the valuation of the firm, and the related costs to the VC of participating with the firm 228, and the firm's exit strategy from the market 230.
The forecasting process 120 may result in an initial prediction 130, which is then subjected to error analysis 140. At this point, the prediction may be subject to a requirements analysis 150 to determine whether the prediction meets requirements set down by the stakeholders. The process may continue to improve the initial prediction by subjecting it to further prediction refinement, error analysis, forecasting, and requirements analysis.
Thus, the final prediction 130 is reached through analysis and deliberation. Because this process involves a high degree of deliberative subjectivity, VC projections of an investment's success can be rife with error, even if error analysis is part of the equation.
Two problems may compound the difficulty of decision-making. First, collaborative groups tend to gravitate to the mean of their collective knowledge, without aggregating it. Second, such groups are easily politicized, resulting in risk aversion among their participants. The results can be a skewed perspective among the group, and predictive failure.
Similarly, an IP asset holder may go through a decision-making or valuation methodology when determining how best to value or exploit their IP. In order to exploit an intellectual asset optimally, the IP asset holder may utilize material input and participation from both early-stage finance sources and the marketplace.
Exemplary considerations 300 for an IP asset holder are detailed in FIG. 3. Each constituent in the IP value chain may utilize information from the other constituents in order to maximize value throughout the chain. The IP holder may therefore wish to determine information related to the application of the asset to the marketplace 310, including customer demand(s) 312, competitive initiatives 314, requirements for further development or refinement 316, pricing 318, ongoing R&D mandates 320 and asset life-cycle 322. The IP holder may also consider factors relevant to the IP under consideration 340, such as a realistic valuation of the IP in its nascent form 342, both at a pre-revenue stage and while the asset is viable as the basis for an enterprise, and the business prospects suggested by the IP asset 344. Other factors 350, such as pathways to early-stage finance 352, may come into play. For example, such pathways 352 may include equity, debt, or another mix of instruments.
To obtain such information, an IP holder may pursue a program of research involving market studies, focus groups of potential adopters, analysis of comparative enterprises (where visible) and their models, consultative advisory and more. The goal of this process is to derive a cogent business proposition for the asset. Then, once such a proposition is constructed, the IP holder will approach early-stage finance or licensing candidates to pursue the proposition. If possible, this work may be conducted in conjunction with subject experts, both in the discipline of the IP and in all areas of pertinent business logic.
Although this procedure may prove useful in the decision-making process, it is often abrogated in the real world for a number of reasons. For instance, market studies, surveys, focus groups and soft launches, where professionally designed and implemented, are expensive and time-consuming. Further, market research may not provide optimal aggregation of the market's perspective on an asset. Rather, it acts like a poll, providing feedback on aspects of the proposition (product makeup, pricing, consumer targeting, etc.), and serves as a subjective (and potentially helpful) snapshot of the market's potential bearing. However, market research does not necessarily provide a comprehensive picture of the viability or success calculus of the initiative. As such, research can make stakeholders feel better and more informed about the IP's prospects, but the risks associated with the IP may not be materially improved by this procedure.
IP originators such as research universities, national laboratories, corporate entities, and early-stage investors can suffer from an inefficient and suboptimal methodology for analyzing, evaluating, valuing and exploiting intellectual property or new product concepts. These parties in the IP value chain, and the marketplace they ultimately seek to address, may lose out on material opportunity and profit as a result.
Taken together, these problems of economics and utility may make the pre-launch research process (upon which early-stage financiers can rely heavily) problematic. These problems may be compounded in the case of institutions with a variety of potential assets, each of which might require pre-launch research. The result may be a materially unexploited IP asset or product, leaving substantial economic benefits unrealized.
Further, decision making is becoming increasingly complicated. For example, markets continue to evolve with increasing speed and complexity, and as opportunities for investment arise in more esoteric forms (and with higher levels of competition for participation), there is a need for an improved decision-making process for selecting investments.
There is a need for a systematic approach to decision-making and valuation so that decision-makers may obtain a clear view into the real prospects, value and market applications of the objects of their decision-making (such as IP). The present invention relates to a predictive wiki optimization application that meets this need by systematizing and formalizing the valuation and decision making process.